Comparative analysis of molecular fingerprints in prediction of drug combination effects

Zagidullin, Bulat; Wang, Ziyan; Guan, Yuanfang; Pitkänen, Esa; Tang, Jing

doi:10.1093/bib/bbab291

Cited by 62 publications

(48 citation statements)

References 98 publications

(71 reference statements)

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“…7,[19][20][21] Previous reports vary in their conclusions on whether the implementation of chemical descriptors, rather than generic one-hot encoding (OHE) representations, truly boosted the predictive performance of their ML models. 19,22,23 Pdcatalyzed C(sp 3 )-H activation is a powerful reaction manifold that enables the facile introduction of functional complexity in small molecules, in addition to the late-stage functionalization of complex molecules like trimipramine (1), a tricyclic antidepressant, as recently demonstrated by one of our groups. 24 Hence we chose to explore the parameterization and featurization of the newly developed tertiary amine directed C(sp 3 )-H bond activation with a HTE-generated dataset, comparing tailored descriptors, based on in silico studies, to understand the influence of descriptor complexity on supervised ML prediction and closed-loop optimization.…”

Section: Introductionmentioning

confidence: 99%

The effect of chemical representation on active machine learning towards closed-loop optimization

et al. 2022

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Section: Introductionmentioning

confidence: 99%

The effect of chemical representation on active machine learning towards closed-loop optimization

et al. 2022

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“…This makes it a powerful 2/22 approach to develop models that are able to predict drug synergy based on drug combination screening experiments and other relevant data. Several ML models for drug synergy prediction have been described in the literature [8,[11][12][13][14][15]. Many of these studies used tree-based ML methods, such as random forests (RFs) [11,12,14] or gradient boosting [12,13,15].…”

Section: Introductionmentioning

confidence: 99%

“…Given that the screening datasets that are currently available only contain a very limited number of compounds, it is still unclear whether there is any benefit in using learned representations instead of traditional fingerprints and descriptors. A recent study benchmarked several compound representations on a large drug synergy dataset and found that DL-based representations were able to outperform traditional fingerprints [15]. However, the authors also noted that the difference between the top performing DL-based methods and the best fingerprints was not substantial and that other concerns, such as interpretability, may be more important.…”

Section: Introductionmentioning

confidence: 99%

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A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

Baptista

Ferreira

Rocha

2022

Preprint

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One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact on performance. Drug features appeared to be more predictive of drug response. Molecular fingerprint-based drug representations performed slightly better than learned representations, and gene expression data of cancer or drug response-specific genes also improved performance. In general, fully connected feature-encoding subnetworks outperformed other architectures, with DL outperforming other ML methods. Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.Author summaryCancer therapies often fail because tumor cells become resistant to treatment. One way to overcome resistance is by treating patients with a combination of two or more drugs. Some combinations may be more effective than when considering individual drug effects, a phenomenon called drug synergy. Computational drug synergy prediction methods can help to identify new, clinically relevant drug combinations. In this study, we developed several deep learning models for drug synergy prediction. We examined the effect of using different types of deep learning architectures, and different ways of representing drugs and cancer cell lines. We explored the use of biological prior knowledge to select relevant cell line features, and also tested data-driven feature reduction methods. We tested both precomputed drug features and deep learning methods that can directly learn features from raw representations of molecules. We also evaluated whether including genomic features, in addition to gene expression data, improves the predictive performance of the models. Through these experiments, we were able to identify strategies that will help guide the development of new deep learning models for drug synergy prediction in the future.

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“…Research [13] proposed a method called DeepConv-DTI that applies convolutional neural network (CNN) in predicting binary classification DTI using amino acid composition (AAC) as protein features and circular fingerprints as compound features by analyzing compound's molecule as a graph. Choosing the right type of fingerprint to represent the features of the compound is important in the process of searching for potential drugs [14].…”

Section: Introductionmentioning

confidence: 99%

Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

Fadli

Kusuma

Annisa

et al. 2021

BDCC

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Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an efficient and economical approach. Research in drug repurposing can be done by observing the interactions of drug compounds with protein related to a disease (DTI), then predicting the new drug-target interactions. This study conducted multilabel DTI prediction using the stack autoencoder-deep neural network (SAE-DNN) algorithm. Compound features were extracted using PubChem fingerprint, daylight fingerprint, MACCS fingerprint, and circular fingerprint. The results showed that the SAE-DNN model was able to predict DTI in COVID-19 cases with good performance. The SAE-DNN model with a circular fingerprint dataset produced the best average metrics with an accuracy of 0.831, recall of 0.918, precision of 0.888, and F-measure of 0.89. Herbal compounds prediction results using the SAE-DNN model with the circular, daylight, and PubChem fingerprint dataset resulted in 92, 65, and 79 herbal compounds contained in herbal plants in Indonesia respectively.

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Comparative analysis of molecular fingerprints in prediction of drug combination effects

Cited by 62 publications

References 98 publications

The effect of chemical representation on active machine learning towards closed-loop optimization

The effect of chemical representation on active machine learning towards closed-loop optimization

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

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